CDI-Type I: A science and engineering programming and inverse modeling environment for massively parallel heterogeneous computing systems

CDI-Type I：大规模并行异构计算系统的科学和工程编程和逆向建模环境

• 批准号: 0941666
• 负责人: Martin Saar
• 金额: $ 60.41万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941666&HistoricalAwards=false
• 关键词: CDI; Type; science; engineering; programming

The goal of this project is to provide scientists and engineers with a generalized, hardware-independent programming and runtime environment that takes advantage of the wide range of scalable, high-performance, highly heterogeneous hardware systems available, while masking the details of each from the programmer. This environment will further provide an integrated inverse modeling tool to perform guided parameter space and uncertainty analysis as well as model optimization. Such inverse modeling is critical to identify general patterns in order to develop new scientific theories that characterize complexity and thus capture the essence of complex natural and engineered systems. In addition to improving our understanding of complexity in natural and built systems (primary CDI theme), the proposed environment will aid in visualizing and extracting knowledge from data (secondary CDI theme) for which inverse methods (e.g., deconvolutions, regressions, parameter space/uncertainty analysis, model optimization) are indispensable. Reaching these goals is only possible, however, when combining the highest-performing heterogeneous hardware solutions that can execute many thousands of simulations with a high-level programming and inverse modeling environment that effectively hides the heterogeneous hardware complexities. Several general computing classes are identified, with example applications from science and engineering, that are likely to benefit from such heterogeneous hardware implementations. Such implementations would then open up new, transformational opportunities in scientific and engineering computing, increasing the likelihood of discovery of new theories regarding multi-scale interactions, emergent behavior, pattern formation, and self-organization in complex, feedback-prone systems.Several fundamental scientific and engineering computing categories with example applications from seismology, volcanology, hydrodynamics, and rock magnetics have been identified as targets of opportunity for this type of heterogeneous computing.

该项目的目标是为科学家和工程师提供一个通用的，独立于硬件的编程和运行时环境，利用广泛的可扩展性，高性能，高度异构的硬件系统，同时掩盖每个程序员的细节。该环境将进一步提供集成的逆向建模工具，以执行引导参数空间和不确定性分析以及模型优化。这种逆向建模对于识别一般模式至关重要，以便开发表征复杂性的新科学理论，从而捕捉复杂的自然和工程系统的本质。除了提高我们对自然和建筑系统（主要CDI主题）复杂性的理解外，所提出的环境还将有助于从数据（次要CDI主题）中可视化和提取知识，其中逆方法（例如，解卷积、回归、参数空间/不确定性分析、模型优化）是必不可少的。然而，只有将能够执行数千次仿真的最高性能异构硬件解决方案与有效隐藏异构硬件复杂性的高级编程和逆向建模环境相结合，才能实现这些目标。确定了几个通用计算类，从科学和工程，可能会受益于这种异构的硬件实现的示例应用程序。这样的实现将为科学和工程计算开辟新的变革机会，增加发现关于复杂反馈系统中的多尺度相互作用、涌现行为、模式形成和自组织的新理论的可能性。和岩石磁性已经被识别为这种类型的异构计算的机会目标。